On multimedia recommendation sites

I'm developing a movie recommendation site at work and it has been the most entertaining project I have had in years, but no one seems to care about it so I'm thinking of hosting my own version, perhaps focused on anime/video-games. I was wondering if anyone (other than me) would use it. The "website" is basically just a receptacle of annotated dataset, just like danbooru. The "fun" part about it is how to decide if something garbage using unsupervised learning and how to keep the database fresh without having to manually review every single item by hand.

>>13203 something like that and being able to index and query by media type would be really great, especially if you could also make a viewer to locally download stuff you want to consume

>>13204 > being able to index and query by media type This is just a tag (e.g., "media_type:movie" so that one is easy. > make a viewer to locally download stuff you want to consume The best I can do is link a streaming/torrent site since hosting all the content myself would be unfeasible.

could be neat, if its good and has coverage of things I'd find interesting.

>>13203 Hmm well if I want to find new movies i would browse letterboxd, and something like r/animerecommendations for anime, and >>>/inter/ for games. The only way i see algorithms can be better than human curated recommendations is when you can feed a lot of data into it, but that requires you to build a list of what you've watched/played somehow. And only then it becomes useful. There's already stuff like anilist for anime. So I guess if you want your recommends to be useful you would want to integrate as many of these movie/anime/game tracking platforms as you can.

Picrel is what I have in mind. A list of 1000-2000 pieces of media, tags, and trailers. I'm not sure if adding streaming links would be useful or not. Personally, I just visit any torrent site and download what I need but maybe others would like to know if a particular item is available in their favourite streaming site. >>13208 > things I'd find interesting. And what would that be? >>13208 >The only way i see algorithms can be better than human curated recommendations is when you can feed a lot of data into it, but that requires you to build a list of what you've watched/played somehow. And only then it becomes useful. That's user-based filtering. There is also content-based filtering (using a formula to quantify "quality", asking you to fill a form, ...). The problem with user-based filtering is that, as you said, requires a lot of data and also creates the so-called "echo chambers". > So I guess if you want your recommends to be useful you would want to integrate as many of these movie/anime/game tracking platforms as you can. Yeah, that's the plan.

There used to be a site called Tastekid that would recommend things based on what other people who liked stuff you like liked. It got bought out and changed to Tastedive.

https://mako.st/ It's done. An easily searchable database of somewhat good anime. The site is quite basic but functional. I will be downloading more databases and adding more content but I would like to know what do you guys think. Most items in the front page are particularly niche because I sort by new so if you don't trust a high rating you can either browse a little or make a more specific query. https://mako.st/content?tags=shoujo+historical https://mako.st/content?tags=seinen+comedy https://mako.st/content?tags=action+mythology

Picrel is an illustration of how the algorithm works. So, basically, if one person gives a high rating to a particular item, that doesn't mean anything. Now, 10k positive reviews? Now that's something. We can't decide if a rating is good or bad in the vacuum; we only know a piece of media is "good" if it's better than most of their peers. In particular, we can say that a particular item is "above average" if its rating is above average and if it is somewhat popular. The average rating here is around 7 points so with a rating above 7.5 and 10k votes we can be sure the The main problem arises at the decision boundary. Do we include an item if it has an average rating but it's extremely popular? Do we include an item with a good rating but few views? Where do we draw the line? The solution: kmeans. I assumed there are four groups to filter 75% of the dataset. The groups: > great (high vote count, high ratings) > popular (high vote count, mixed ratings) > niche (low vote count, mixed ratings) > trash (low vote count, low ratings) And that's a quick and dirty way to quantify good taste.

>>13216 I added percentiles in addition to raw ratings/votes since saying how does it compare with the rest of the database is more informative than the raw numbers.

Added autocomplete and additional tags. If what you see doesn't look like picrel when hard-reload (Shift+F5) the page. Concerning picrel, I found it while browsing the most recent additions. It seems to be inspired by the Romance of the Three Kingdoms (lit. "日本三國"). The rating is abnormally high and it should keep steadily increasing while the series is airing because casual watchers that wait until something is popular to consume it are more lenient and give better ratings. An example of this phenomena: - https://archive.ph/nvvQ7 - https://archive.ph/Zz43b - https://archive.ph/AoLcS - https://archive.ph/GCT7T - https://archive.ph/efLU3 - https://myanimelist.net/anime/52991/Sousou_no_Frieren (the bubble pops and the rating stabilises) Now, to detect the bias we can simply compare it with similar pieces of media: - https://mako.st/content/golden-kamuy-2018 (7.89, 91.9%) - https://mako.st/content/kingdom-2012 (7.87, 91.6%) - https://mako.st/content/nippon-sangoku-2026 (8.45, 98.2%) To approximate the real rating we can use the sandwich theorem if we provide a lower bound and an upper bound. For example, Nippon Sangoku is worse than SnK, and better than SAO. So the real score should be between 7.5 and 8.5. This method is very accurate but, unfortunately, a computer can not use it. Therefore the best way we have to mitigate bias is waiting until enough people rate the item and diversifying our sources. I wonder if this bias is caused the public nature of the ratings. An increase of the rating causes an increase of the rating (feedback loop) because it's very hard for the crowd to go against the consensus so the first votes have a very strong influence over the final rating. Some sites try to mitigate this with Bayesian methods but, as you can see, it's not perfect. By the way, I wanted to blogpost about the algorithms and data structures I used for the recommender algorithm but that will probably bore most people.

I noticed that someone got derezzed because their browser kept requesting missing resources. This was caused by an incorrect configuration on my end. I apologise for that; It should be fixed now.

I improved the autocomplete algorithm. Now it should match any word prefix in titles. This means that titles like "Toujima Tanzaburou wa Kamen Rider ni Naritai" will show up if you write "kamen" or "kamen-rider". Also, if there is a single result, you will be automatically redirected to the details page: https://mako.st/content?tags=kamen-rider https://mako.st/content?tags=best-1970s I'm using a trie to efficiently store and search prefixes. To match substring prefixes I first tried simply adding all valid prefixes (4000*4000 = 16M prefixes). Unfortunately, this consumes too much space so I decided to only add the prefixes that are followed by a separator ("-"). This means we can not match "ical" in "Magical", for example.

Added a SVG with a visual representation of the distributions. The plot is a fitted theoretical PDF so the percentiles might not always match. The distribution of the votes, in particular, looks like an "L" so picrel is the best I could come up with. The percentiles are always exact. That should be enough to answer if a particular piece of media is Good (with a capital "G") or not. Still, we have another problem. To know what's the anime about you have to: a) Decode ambiguous tags b) Read a wall of text* The solution is the same: unsupervised learning. We vectorize the content and try to group items together. I chose cosine similarity and the Louvain method to create pertitions. It worked reasonably well because similar media have similar tags. The clustering/partition algorithm also finds indirect/transitive relationships so results are both interesting and explainable. The final "product" of this analysis is a new set of tags, "[tag1]-[tag2]-like-[slug]" which shows common tags in the cluster + the most popular/representative item. That gives us self-explanatory tags like "romance-comedy-like-kaguya-sama-wa-kokurasetai-tensai-tachi-no-renai-zunousen " (there are a lot of these, by the way). Smaller clusters do not have their own tag but the most representative items are included in the stats section. * I tried using this wall of text (the synopsis) to form groups (using embeddings, &c). It did not work. I sill have to try creating the vectors using bigger models but I believe that the actual problem is that the synopsis says what happens but doesn't create a picture of what's the story is about. Reviews have a different problem. It looks more than the users are trying to farm internet points than trying be genuinely helpful. I suppose that's normal I don't see how can I use it to help me (us?) decide if I should watch/read something.

I will update the database in around two weeks and add new titles. In the meantime, I going to play with anubis (a Web Application Firewall) and my own tools to reduce the number of bots. Not because the overload the site but because they fill the logs with garbage requests. Some particularly annoying crawlers rotate headers and referrer links so it is impossible to extract any useful insight from them except, perhaps, HTTP errors. This might explain why client side analytics are more popular.

I have discovered a fundamental flaw in the way I've built the recommendation/filtering algorithm. Since I focused most of my efforts in removing bias, the results have little variance. This means the algorithm will recommend, with absolute precision, what a random person will almost surely like. This means the results are good but not very interesting. For example, a pure content based algorithm will never recommend you trash like https://myanimelist.net/anime/59835/Kirio_Fanclub or https://myanimelist.net/anime/44942/Shuumatsu_no_Walk%C3%BCre. And that's a problem. I need to, somehow, create a mathematical model of soykaf taste. I don't think collaborative filtering is the answer but I'm definitely going to need user data for this because otherwise I would be simply reducing the global quality of the database. Now, recommending trash (items in that could be either great or terrible; usually in the decision boundary of the algorithm) is an order of magnitude harder because, as mentioned, it's trash so most people won't like it. To increase our chances, we can try to use information about other users ("people who bought X also bought...") which is basically collaborative filtering. The problem is, of course, feedback loops. We can either try to mitigate the feedback loops or try something new. I will review the literature again and see if I can come up with a decent solution.

>>13203 >>13244 You should consult existing literature on the topic, starting with the relevant Wikipedia articles. https://en.wikipedia.org/wiki/Collaborative_filtering https://en.wikipedia.org/wiki/Matrix_completion

>>13246 The wikipedia article on collaborative filtering is pretty good but I think I will just download all papers that discuss recommender systems and rank them using my content-based algorithm. In other words, I'm going to use a recommender system to learn about recommender systems.

You can now sort by any of the numerical fields. https://mako.st/content?tags=sort%3Arating+asc https://mako.st/content?tags=sort%3Arelease_year I will probably add some kind of shortcut for this to the UI.

I'm considering using anidb as my main source because they provide a time series with the number of votes vs the average rating. This data is interesting because we can see how the first ratings cause a strong bias, and how the average rating diverges with time. The value usually goes down as hype dies or continues growing steadily as new people are exposed to the content but it never reaches a stable point (i.e. the "real rating"). This means a small group of people can cause a permanent, long-term effect in the "final" rating. So if you observe an absurdly high average value in anidb/MAL it could be simply luck + a feedback loop. PIcrel is an example of an absurd rating (jojo) and two examples of series diverging (SAO and Aoi Bungaku). So we have two sources of noise: an "elite" group that gives the first few ratings, and the crowd. Neither the first score nor the sign of the derivative (path of the average score) are good signals. As a final case study, here is Shuumatsu no Walkure, another example of the effect of the first rating. https://anidb.net/anime/15932/vote/statistic Rating (2021-06): 2.7* Rating (2026-05): 3.50 By the way, anidb seems to have two types or ratings (permanent and temporary) but it doesn't change anything because we only care about the properties of the time series. * The global average in anidb is 7 according to google

New update. Added 10 items including seasonal anime (Akane-banashi, Marriagetoxin, Ganbare! Nakamura-kun!!) and old anime that got picked up by the algorithm (Master Keaton, Zettai Karen Children, Gakuen Utopia Manabi Straight!). Most of you probably know recognise the most recent titles so I will talk about a borderline unknown title. > https://mako.st/content/master-keaton-1998 Master Keaton. Released in 1998 along with Cowboy Bebop, Cardcaptor Sakura, and certain sci-fi anime that's somewhat popular among computer enthusiasts. The average rating for this title increased significantly since last month, from 7.68 to 7.75. An increase of 0.07 points in one month is strange considering it aired more than 20 years ago. The rating is almost at the 90 percentile and no one at MAL (my main source) has bothered to write a bad review. I'm curious to see if mediocre popularity is enough to mitigate rating manipulation. I'm probably going to watch it to verify if the rating is correlated with the actual quality. I also removed 30 items including Busou Shoujo Machiavellianism, The iDOLM@STER Cinderella Girls, SAO: GGO II, and a bunch of Chinese anime. Some of these might be good, but there is not enough evidence to prove it. The next season (Summer 2026) doesn't start until July, so the update is mostly housekeeping. That said, we can observe new episodes have caused a lot of volatility. The rating Otonari no Tenshi-sama (second season) experienced a huge crash (-0.35 points). This puts it below its prequel (at 7.86 points). A blunder like this is quite rare, most second+ seasons receive a better rating because those who do not like the concept presented will get filtered by the first season. For example: - https://mako.st/content/kingdom-2012 (7.87, 91%) - https://mako.st/content/kingdom-2nd-season-2013 (8.29, 96%) - https://myanimelist.net/anime/44942/Shuumatsu_no_Walk%C3%BCre (6.84, ~50%) - https://mako.st/content/shuumatsu-no-walkure-ii-2023 (7.49, 81.2%) There is little difference between the content presented in each season so the subjective quality alone doesn't justify the difference. Class de 2-banme ni Kawaii Onnanoko is now at 7.90 for an increase of 0.25 points. This makes it the best rated romance anime of the current season. The rest of the increases/decreases are shown in picrel.

>>13249 >>13254 Both of these feel a bit superstitious with unsound methodology. Are you sure you understand how hypothesis testing works (and the scientific method in general)? I would recommend to at least think about what the null hypothesis is before going balls deep seeking confirmation for every conspiracy theory that you can imagine.

>>13259 >Both of these feel a bit superstitious with unsound methodology. Yeah, I was thinking the same thing. I appreciate that you pointed that out because I probably wouldn't have made a more rigorous study otherwise. What I want to prove: - Herd behaviour - First ratings predict the rating after 3 months (finished airing) - The average rating never converges To prove herd behaviour, we can use autocorrelation as a proxy. That is, prove that new ratings are correlated with previous ratings. The null hypothesis here is that there is no significant autocorrelation. I used the Ljung–Box test on the time series of average ratings of the winter season of 2024 and all of them were autocorrelated (p < 0.001). Still, I think this might be a somewhat trivial result because of bayesian rating the fact that so I'm looking for raw ratings + timestamp to improve the experiment. For the second hypothesis, we can simply plot the average rating after a week and the average rating after the series has supposedly finished airing. If the regression model predicts the final rating then the first ratings influence the final rating. I think this result is a bit more surprising because those who rate the item after just one week have only watched the first episode! Still, I want to try to prove it with using Pearson correlation or any other more serious test because drawing a line is not a proof. Dataset: Anilist public API Related papers: - https://snap.stanford.edu/class/cs224w-readings/muchnik13bias.pdf - https://d30i16bbj53pdg.cloudfront.net/sites/default/files/media/leehosanagartan.pdf

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>>13262 >What I want to prove: >- Herd behaviour >- First ratings predict the rating after 3 months (finished airing) Can you prove it is necessarily herd behavior? It could be said that it is not herd behavior, but merely people having similar aesthetic taste such that they will give similar reviews. What would be more interesting and I think closer to your point, is to somehow measure how the 'spread' of reviews changes over time. You could use that type of graph they use in stock markets, as that visualizes percentile distribution at a point in time Of course you'd need to drop the use of 'average rating' for lifetime, any part of the graph should represent the spread in just that time period. The results would be pointless otherwise. I am curious if for your sao_ii.png visualization whether the reviews local to the 2019 period are actually much worse than the average rating would imply The very large changes you observe early in the anime lifetime seem to me a natural consequence of having a low amount of review data.

>>13222 >>13249 >>13262 The obvious solution is to do an experiment. People have to be able to give a rating on your site though. Some entries will show the established (average) rating, and others don't. Then, you can compare whether the shown rating changes the subsequent ratings.

>>13265 Oh, A/B testing. Yes, that's perfect. >>13264 >Can you prove it is necessarily herd behavior? Nay. I need the user ratings (i.e., time series with user_id, anime_id, rating, date) and that's sensible data so public APIs do not expose it and I haven't found a way to scrape it. >how the 'spread' of reviews changes over time. You could use that type of graph they use in stock markets >spread I believe the term you are looking for is variance/standard deviation, or perhaps the range of values. >that type of graph they use in stock markets >pic Boxplot. What you are describing is a test of skewness. That is proving that the probability mass of the distribution is larger in one side. Picrel. IIRC the one papers I linked used it verify herd behaviour. The distribution should be normal so if it's skewed there is some kind of bias.

>The distribution should be normal so if it's skewed there is some kind of bias. Should it? For one, you can't have a normal distribution on a finite interval, it will always be skewed if the average rating is near the maximum or minimum. There are also many shows that people either love or hate (giving two peaks). I don't see how an "average" show (with an average rating of 3) that more people like than dislike illustrates herd behavior.

>>13266 Ahh, thanks for explaining. I have a basic knowledge of statistics, I can intuit some of it but can't properly communicate it. My hypothesis is that if there is herd behavior, you would probably see it if you were to take the distribution of blocks of time, plot the variance over time (IIRC variance is 2 times width of a standard deviation?) for the ratings of an anime, and see if it decreases over time. It's a shame the data is not available because this question has very much interested me now.

>>13267 It's a truncated normal. I've never seen a bimodal (i.e. with two peaks or modes) rating distribution. The most pathological case I found is School Days and even that one doesn't have two peaks. https://myanimelist.net/anime/2476/School_Days/stats >I don't see how an "average" show (with an average rating of 3) that more people like than dislike illustrates herd behaviour. If the average rating is below the global mean, we can say that "more people like than dislike" the show, yet this doesn't say anything about the symmetry of the distribution itself. An ideal rating process makes three assumptions: 1. Independence: ratings do not influence each other 2. No bias: The expected value of the observations is the "true rating" 3. Symmetric errors, centred in zero: This means under-estimations and over estimations are equally likely. This is useful because it makes the average (mean) of the errors zero. In other words, if estimations/ratings are unbiased and independent, then the each rating is "Real rating + error", where the error follows a symmetric distribution. Since the average of the errors is zero [3], averaging all scores gives us the real rating, which is what we want. If the ratings influence each other or there is any kind of systematic bias, then the rating becomes "MyOpinion(Real rating, bias) + error" (the formula is merely illustrative). This means the average rating is no longer a good estimator of the real rating. Now, does systemic bias imply skewness? Not at all. However, we only care about the case where there is herding. Herding is a feedback loop (positive or negative). This means users will either more likely to under-estimate or over-estimate. The result? An asymmetric error. Hence, herd behaviour implies skewness.

When looking for an specific title, prefer to the use romanized name since that's how items are identified in MAL. Still, I'm going to see if adding alternative titles doesn't increase the memory fingerprint that much so you can use either the localised title as well. For example, if you are looking for Ghost in the Shell you would write "koukaku-kidoutai" in the search bar. https://mako.st/content/koukaku-kidoutai-stand-alone-complex-2002

I wanted to verify if new anime consumers were more lenient so I calculated the mean score per year (picrel, the Confidence Intervals assume normality). MAL was launched on 2004 so there might be missing items from the previous years, affecting the mean. What's interesting about this is that I expected to see the average steadily increasing but instead we observe that the average plummets after MAL was purchased for the first time (2008) and then it begins to grow again after 2017. The growth in the last decade might be related to the widespread adoption of streaming services but it's hard to know for sure. There is little correlation between the year and the rating* so what we observe might be a result of chance. * Pearson: r=-0.0688 >>13269 I asked them for a sample but I haven't received any response yet. It's not the first time I have to annoy someone for hours/days/months to get a dataset so we should have our answer eventually.

I have been experimenting with other kinds of media. In particular, recent Vocaloid releases. Every three days, more than three hundred MVs are released on youtube*. Of these, after the first day, 30-50% have exactly zero views/likes, most have less than a hundred views, and a select few have more than ten thousand views. When many items have very low values and few have very high values it's usually because the phenomena follows a power law; that is, the number of views grows exponentially with the "ranking" of the video. The difference between the first and second places might be larger than the total of people who watched the video in the the tenth place**. Obviously, even if it's just a few hundred videos, no one wants to watch them all just two download 0-2 of them. Therefore, we need recommendations. Youtube, as you probably know, is terrible recommending content. My hypothesis is that the problem is that their algorithms optimise the "time the user spends in the platform"--and they are great at it!--instead of user satisfaction. So m[spoiler][/spoiler]y solution was to use a simple heuristic, like/view ratio multiplied by log(views), to sort all videos and select the top 10% (arbitrary number). I added the second factor to penalise videos with too few views. I have been testing it for a month or two against real recommendations made by humans. The precision is high and the recall is okay-ish. This means that the 2-3 videos selected by the algorithm are genuinely good but the algorithm doesn't find all the good videos. Another problem is that the algorithm needs some "warm-up" time (i.e. wait until the sample size is statistically significant) so it's slower, in a sense, than human recommendations. I'm not completely satisfied with the results but it's good enough for me at the moment. Better than clicking random thumbnails on Youtube for an hour. * I'm only counting those featuring 重音テト and a few wholoids I selected ** this is merely illustrative, of course, since the relationship holds for quantiles and but not necessarily "ranks".

>>13244 I tested several algorithms using popular kaggle datasets* and Recall@10 while leaving-one-out as a metric. Collaborative filtering (CF) performed a little better than the baseline and content-based (CB) recommendations where in the middle between the baseline and CF. The most surprising result was that, when predicting items that received a 10 (max rating), the baseline, raw popularity, was just as good as CB and nearly as good as CF** CF, as you can probably guess, uses more data than pure content-based algorithms. Since these models use way more data and resources I wanted to know if the additional precision justifies the costs. I chose leave-one-out with recall as a benchmark because this is roughly equivalent to a "reconstruction error". This means the metric measures how good the model is guessing a missing item from a list (in this case, an anime list) which is, as well, roughly equivalent to predicting the next item the user will choose (next anime the user will watch), which is more-or-less what we want. This kind of "chain" might be confusing but it's common in machine learning. We don't have an oracle that tells us if a recommendation is good or not so we have to approximate it with what we have. This means the results are always inaccurate. The first source of error is the error we introduced merely by selecting the evaluation metric. After selecting a metric the next step is choosing the baseline. The model chosen in this step is usually some trivial statistical model. In this case, the model chooses a random item using the rating count as weights. This might feel like cheating because the rating we want to predict is included in the total votes but I thought that subtracting the user's vote (1) was a bit silly. Simple cosine similarity was worse than the random model (uniform) with a score of less than 0.01. However, after combining it with popularity (the baseline) it performed much better (Recall@10: 0.18)***. Alternating Least Squares (ALS) was better with a score of 0.34***. I tried tinkering with the CB algorithm (e.g., adding the release year and demographics to the formula/vector) and I discovered that predicting anime with a score of 10 yielded better results, and more importantly, I noticed that everything but popularity was added noise to the prediction. I concluded that recommendation algorithms are not that great without a lot of data (i.e., what >>13208 said). And I'm not completely sure the recommendations will be significantly better even with a lot of data. [ Bonus ] Recommender systems can be used to predict ratings as well. (see https://github.com/nicolashug/surprise). The standard deviation in movie-lens-small (the dataset I used to verify the results) is around 1.0, the mean is 3.5 and 50% of the data is between 3 and 4. The best model reports an error of "0.737" (MAE), which probably larger than difference between the best film you have watched and the worst. In other words, I don't see how it's an improvement over the baseline considering the amount of data it needs. * https://www.kaggle.com/datasets/CooperUnion/anime-recommendations-database/data A bigger dataset exists but I decided to start with this one to speed up the analysis. ** Recall@10: - CB: 0.2 This is my own hybrid model that combines raw "popularity" in form of the probability that the user selects the item at random given the total number of ratings given and cosine similarity using the tags. - CF: 0.3 I used Alternating Least Squares. Not SOTA, but close. - Baseline: 0.2 This model simply selects popular items at random. A score of 0.2 means that it guessed a hidden item of the user's list roughly 20% of the time. *** In this case we were predicting items in the user's list with a rating higher than 7.0 (i.e. "good") >>13254 >I'm probably going to watch it (Master Keaton) to verify if the rating is correlated with the actual quality. I noticed something funny while I was watching this. Usually, when I (speed)watch something for the first time, I look for a reason to continue watching. In this case, I was looking for a reason to stop watching. I implicitly assumed the anime good because of the high rating. In other words, I can't tell if the anime great or just okay because I can't trust my own judgement.

New update. Added 45 titles including Super no Ura de Yani Suu Futari and Ponsuka. The first one was released this month and it really stands out because of its crazy high rating and (comparatively) huge amount of votes. > https://mako.st/content/super-no-ura-de-yani-suu-futari-2026 A romance seinen for ojisans featuring ojisans (this "genre" has been quite popular lately). In less than a month it has surpassed most of the titles of the season and, if this positive trend continues, it might beat Nippon Sangoku, one of the top contenders of the current season and possible candidate for the anime of the year 2026. The concept is not completely original but the execution is good enough. I also removed 38 titles, including Jahy-sama wa Kujikenai!, Tsugumomo and Umineko no Naku Koro ni. You might notice that some of the titles that were added in the last update were removed. Some of these actually lost votes (!) and rating this month. This is interesting because I can't explain this kind of volatility nor the hundred of votes that suddenly disappeared*. Most titles finished airing this week as far as I know so there is some volatility in the ratings. That said, the distribution is not much different than what we saw last month, at least visually. The current season of Honzuki no Gekokujou (Ascendance of a Bookworm), lost 0.14 points and it's now far behind the previous seasons. Recall that new seasons usually have better rating and less votes so this implies the quality might have dropped**. Youkoso Jitsuryoku Shijou Shugi no Kyoushitsu e (Classroom of the elite) lost 0.9 points, less than last month but still negative. For some reason, I don't find this steady decrease in the rating surprising. The number two winner of this month is Re:Zero and I'm only talking about the number two first because it's about to become the highest rated anime in MAL, which is hilarious. I believe it gained a boost after returning to its roots: torturing the protagonist. In any case, this shows how randomness and bias can impact ratings. The current season of Re:Zero isn't significantly better than the first one and yet the rating is an order of magnitude higher. Ganbare! Nakamura-kun!! gained 0.34 points and it's now the second most highly rated anime in its genre (yaoi). The next update will cover the next (current?) season, Summer 2026. * In particular, Master Keaton lost at least 411 votes. ** I can't confirm because I dropped this one years ago

Summer 2026 brings a few interesting prospects. Koukaku Kidoutai received (yet) another adaptation. I dropped it immediately when I first watched it because of how much this new adaptation differs from the movies. My first impression is that it was a bastardization of the source material but, after researching a bit, I discovered that this actually a faithful adaptation and the movies were not. The rating was quite outstanding all things considered** so I realized I had, perhaps, been too quick to judge. Indeed, the new adaptation is an artist's work; if you dropped it before you might want to give it another chance. Otome Kaijuu Caraméliser is a romance shoujo* about a JK that transforms into Godzilla when aroused. I believe this line suffices to sell the series to the right people. In general, there are two kinds of ideas: those that are hard to conceive but easy to execute and those that are easy to conceive but hard to execute. This one belongs to the first category so even if the quality is only average the originality compensates. Kimi ga Shinu made Koi wo Shitai also stands out. A series about child soldiers studying battle magic in a girls-only academy. The main focus is the interactions between the characters, not the combat. In a sense, it's similar to Mahou Shoujo Site but, in my opinon, the edge is a bit more "polished" (blunt?). The rating is superb (top 90%) but I have the impression that it should be somewhat higher (around 95%). That said, the fact it mixes cute girls, yuri, and ryona might explain the mixed feelings. This season we get more of Mushoku Tensei which, for some reason, appears to be following the SAO formula of a nondescript protagonist with monotonically increasing harem. Though, to be honest, I never understood what was this series about so I'm probably oversimplifying and filling the holes with cliches. The second season of Youjo Senki and the third season of Grand Blue began airing this month as well. Both are more of the same, which is, in this case, a good thing. It's important to reiterate that while the rating is higher when we compare the new season with the previous one, this increase in the rating doesn't necessarily correspond with an increase in quality. It's just that those who didn't like the previous season (and thus, gave a negative rating) got filtered. Lastly, Aishiteru Game slipped into the database after gaining 20k votes after the last update. These votes caused an increase of 0.11 points in the average rating so it moved from OK -> Good. This one tried to copy Kaguya-sama wa Kokurasetai and failed spectacularly. It would have been an outstanding romcom if it didn't tried to divert traffic from someone else's work. * At MAL it appears as a seinen, which is quite strange. Josei could make sense, but I don't think this qualifies as a seinen under any interpretation. In any case, the cover image should give you a good idea of what to expect. ** Notice that many GitS fans will probably be offended by the contrast between the two versions.

>>13346 >a romance shoujo* about a JK that transforms into Godzilla when aroused So Urotsukidoji.

>>13347 >Hitler and his minions are using the monstrous Nazi Death Rape Machine to render the dimensional boundaries separating the Human World from the World of Man-Beasts and the World of Monster Demons Not quite.

After using my own website for a while, I have noticed that the information in the details page doesn't do a good job helping people (me, in particular) to decide if they want to watch the content. I'm not sure if this is the best way to describe it but I feel there is a lack of "surprise" in the information present in the page. Maybe it's because I'm fully aware that the quantitative measures (e.g., rating percentile) are not guarantees of quality. Maybe it's because the numbers are simply too cold to arouse any kind of enthusiasm. Maybe I just need more numbers. In any case, I'm not satisfied with what I have right now so I'm changing it. Since a purely quantitative approach appears to be insufficient, I'm going to try a more "human" alternative*. What I believe I'm missing it's that someone I trust confirms that the content that caught my eye isn't completely garbage. This is what reviews are for in principle, but as I mentioned before (see >>13232 ), reviews not very useful for decision-making**. This is, perhaps, because art can't be evaluated as objectively as one would evaluate a machine. A machine either works or doesn't work. If you want to buy a certain machine and half of the reviews tell you that the machine is defective, there is no subjectivity. Yet even a 7/10 anime can, and probably is, mediocre. As far as I'm aware there is no solution that mitigates this subjectivity. We can only make trade-offs and play with incentives in a way that the sum of many opinions generate ideas that survive peer-review (i.e., wisdom of the crowds). Since a free market of reviewers competing for internet points doesn't seem to work well I was thinking of a more democratic approach. Instead of competing for the first place in some kind of rating, we could have a single aggregated review with the consensus of the masses. That is, a wiki page***. Since anyone can edit a wiki page, all opinions are taken into consideration and peer-reviewed as opposed as only considering the opinions of those with a long track-record and enjoying a certain degree of popularity as it would happen in a free market. This means the incentives for contributing are not as "strong" as they would be in a free market because the content belongs to many and not just to the "creator" of the initial revision of the review. Since there is only a limited amount of releases every season this could create a friction between contributors that doesn't exist in traditional wikis. This friction could either create an incentive to produce better content or divide the contributors. Regardless of the theoretical trade-offs, I think that giving the weeb elite a space to share their controversial opinions about seasonal anime is a good idea (even if it's just me talking to myself) so I'm adding a wiki to the site. * If this appears inconsistent with what I have said before it's because it is. In hindsight, it seems obvious that neither the data nor the algorithms were the problem. If it were the case, YouTube et al would have figured out how to convert most of the humanity into unwilling ad-watching serfs years ago. Some could argue that they already figured it out but I disagree. ** I remember writing a long post about how the incorrect incentives and the fierce competition in MAL create high-quality yet useless reviews. Perhaps I didn't think it was relevant enough to post it? *** This might seem to be quite a "leap". That's because I came up with this after comparing comparing the different forms of human social organization (e.g., tribes, feudal state, &c) but the full explanation is quite long (and dull).

By the way, it's still early for my monthly trend analysis but: > https://mako.st/content/koukaku-kidoutai-tv-2026 (5778 votes) > https://myanimelist.net/anime/58929/Koukaku_Kidoutai_TV (7459 votes) > https://mako.st/content/otome-kaijuu-carameliser-2026 (5724 votes) > https://myanimelist.net/anime/63150/Otome_Kaijuu_Caram%C3%A9liser (7240 votes) Koukaku Kidoutai is competing in popularity with the Godzilla shoujo romance anime. This is quite strange, I expected a stronger reaction from the public.